System and method for fast digital signal dynamic range reduction using adaptive histogram compaction and stabilization

ABSTRACT

Embodiments are directed to systems and methods to intelligently reduce the dynamic range of a signal. Using a histogram analysis of the signal, significant and insignificant portions of the original dynamic range can be identified. Compaction can then be focused on the insignificant portions of the dynamic range, resulting in significant dynamic range reduction with less signal loss. By compacting the little used portions of the original signal, the dynamic range of the rest of the signal can be largely maintained which results in little loss to signal fidelity, and thus mitigates saturation, quantization, signal mutual suppression, and other issues observed in prior art methods.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/211,420, filed Mar. 14, 2014, which claims priority to U.S.Provisional Patent Application No. 61/785,690, filed Mar. 14, 2013, theentireties of which are hereby incorporated by reference for allpurposes.

BACKGROUND

Embodiments of the present invention are related to digital signalprocessing and, in particular, to dynamic range reduction of signalsusing histogram compaction.

In digital signal processing, a histogram can be used to display avisual representation of a digital signal. For example, in imageprocessing an image histogram can show a tonal distribution of an image.Each bin in the histogram can represent a tone or range of tones and theheight of each bar (which visually represents the value of the bin) canindicate how many pixels in the image have a tone corresponding to thatbin. In video processing, each frame can be represented similarly usinga histogram.

Dynamic range reduction can be used to serve a number of purposes. Forexample, the raw signal data received may be too large to be efficientlyprocessed. Typical imaging systems may not have the resources or time toprocess 14˜16 bit image or video data. Additionally, the imaging systemmay not be equipped with a monitor capable of displaying 14˜16 bit data,and human eyes do not have gray scale resolution beyond 10 bits.

Prior art methods of reducing the dynamic range of a signal typicallyuse a knee curve or smooth curves (such as logarithm/gamma curves)applied to the entire signal. This results in a number of shortcomings.In particular, the dynamic range reduction rate tends to be relativelylimited. Additionally, prior art methods can result in noticeablesaturation in the low and/or high ends of the signal. Quantization canalso be observed where a large amount of the signal falls into thecompacted portions of the signal. A loss of fidelity in the compactedportions of the signal is also observed. Further, the prior art methodsmay result in an unstable output signal, causing the observed outputsignal to fluctuate.

Embodiments of the invention address these and other problems.

SUMMARY

Embodiments of the present invention can be used to intelligently reducethe dynamic range of a signal. Using a histogram analysis of the signal,significant and insignificant portions of the original dynamic range canbe identified. As used herein, insignificant portions of the originaldynamic range refers to those portions of the dynamic range where thereis little to no signal (e.g., bins with low or zero counts), andsignificant portions refers to those portions of the dynamic range whichinclude the signal (e.g., bins with high counts). A threshold value canbe determined, based on an analysis of the histogram, that defines whichbins are insignificant (i.e., have a count less than the thresholdvalue) and which bins are significant (i.e., have a count higher thanthe threshold value).

In accordance with an embodiment, compaction can then be focused on theinsignificant portions of the dynamic range, resulting in significantdynamic range reduction with less signal loss. By compacting the littleused portions of the original signal, the dynamic range of the rest ofthe signal can be largely maintained which results in little loss tosignal fidelity, and thus mitigates saturation, quantization, signalmutual suppression, and other issues observed in prior art methods.Additionally, the processing is self-adaptive to the original signal, sothat the compacted output is stable and maintains its fidelity.

In some embodiments, a method of a dynamic range compaction, cancomprise receiving a digital signal having a first dynamic range,wherein the digital signal comprises a sequence of frames; compacting aframe of the digital signal from the first dynamic range to a seconddynamic range using a compacting function (such as a look-up table)calculated from a previous frame in the sequence of frames; creating ahistogram for the frame and based on the histogram identifying signalstatistics (such as, a centroid and one or more thresholds); applyingthe statistics (such as the one or more thresholds) to each bin in thehistogram to determine a compaction ratio for each bin; determiningcompacting statistics for the histogram; and building a new compactingfunction (such as a new lookup table) from the frame using thecompacting statistics to be used to compact a subsequent frame in thesequence of frames.

In some embodiments, a system for dynamic range compaction comprises asignal capture device configured to receive a digital signal having afirst dynamic range, wherein the digital signal comprises a sequence offrames and a signal processing device configured to receive the digitalsignal from the signal capture device. The signal processing device canbe further configured to: compact a frame of the digital signal from thefirst dynamic range to a second dynamic range using a compactingfunction calculated from a previous frame in the sequence of frames;create a histogram for the frame and based on the histogram identifyingsignal statistics (such as a centroid and one or more thresholds); applythe signal statistics to each bin in the histogram to determine acompaction ratio for each bin; determine compacting statistics for thehistogram; and build new compacting function (such as a new lookuptable) for the frame using the statistics to be used to compact asubsequent frame in the sequence of frames.

Additional embodiments and features are set forth in part in thedescription that follows, and in part will become apparent to thoseskilled in the art upon examination of the specification, or may belearned by the practice of the disclosed embodiments. The features andadvantages of the disclosed embodiments can be realized and attained bymeans of the instrumentalities, combinations, and methods described inthe specification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a frame of an exemplary raw image signal and acorresponding histogram, in accordance with an embodiment of theinvention.

FIG. 2 shows a processed image and corresponding histogram, inaccordance with an embodiment of the invention.

FIG. 3 shows an overview of a method of digital signal dynamic rangereduction, in accordance with an embodiment of the invention.

FIG. 4 shows a block diagram of a method of digital signal dynamic rangereduction, in accordance with an embodiment of the invention.

FIG. 5 shows a method of signal optimization, in accordance with anembodiment of the invention.

FIG. 6 shows an imaging system for digital signal dynamic rangereduction, in accordance with an embodiment of the invention.

FIG. 7 shows a system for digital signal dynamic range reduction, inaccordance with an embodiment of the invention.

In the appended figures, similar components and/or features may have thesame numerical reference label. Further, various components of the sametype may be distinguished by following the reference by a letter thatdistinguishes among the similar components and/or features. If only thefirst numerical reference label is used in the specification, thedescription is applicable to any one of the similar components and/orfeatures having the same first numerical reference label irrespective ofthe letter suffix.

DETAILED DESCRIPTION

Embodiments of the present invention are directed to dynamic rangereduction of signals using a signal histogram to improve signal qualityand stability.

FIG. 1 shows a frame of an exemplary raw image signal and acorresponding histogram, in accordance with an embodiment. As shown inFIG. 1, an input signal can be received which has an input dynamicrange. This input dynamic range may be higher than connected outputdevices can support. In this example, image 100 is a frame taken from atwo-dimensional thermal image signal which has a 14 bit dynamic range.This large dynamic range means that the image signal cannot be properlydisplayed directly on a conventional 8 bit (or even 10 bit) monitor. Inorder to display it properly, the dynamic range of the signal needs tobe compacted (e.g., from 14 bits to 8 bits, which represents a 1/64^(th)reduction in dynamic range).

In accordance with an embodiment, a raw image histogram 102corresponding to the raw image can be calculated and analyzed. Thehistogram can be a full histogram representing the entire frame afterthe entire frame has been received, or a representative histogram whichis calculated as the data streams in. The representative histogram canbe used to quickly identify significant and insignificant bins withoutrequiring that every signal element (e.g., pixel) be sorted and counted.The analysis can include determining one or more threshold values to beapplied to the raw histogram. A threshold value can be used to identifyinsignificant bins (i.e., portions of the dynamic range where there islittle to no signal) as those bins which have a value lower than thethreshold value. In the image processing example of FIG. 1, this maymean that the number of pixels having a tone corresponding to aparticular bin is less than the threshold number of pixels. In othertypes of signal processing, such as audio processing, the thresholdvalue may correspond to a different parameter or feature of the signal.

In accordance with an embodiment, a plurality of thresholds can becalculated for the histogram. When the histogram is analyzed, a centroidof the data can be identified, and a threshold for the left side of thehistogram and for the right side of the histogram, relative to thecentroid, can be calculated. The centroid can represent a weightedaverage of a received signal. For example, in the image processingexamples described herein, the centroid can correspond to a weightedaverage of the intensity of the pixels in a given frame.

The insignificant bins in raw histogram 102 correspond roughly toportions 104 and 106. The insignificant histogram bins can be compactedfirst, limiting the loss of data from significant bins 108 (i.e., thosebins with a value greater than the threshold value). In the example ofFIG. 1, this corresponds to those bins which have more pixels than thethreshold number of pixels, and thus those bins which represent most ofthe image. In accordance with an embodiment, the resulting compactedhistogram can be shaped towards an expected distribution before beingstabilized toward a histogram model adaptive to the original signal. Acompacting function, in this case a look-up-table (LUT), can be createdto convert the original signal histogram distribution to the compactedhistogram model. The data stored in the LUT can be used to shift andscale the significant portions of the original histogram, and compactthe insignificant portions of the histogram, from the input dynamicrange to the compacted dynamic range. Applying the look-up-table to theoriginal signal, results in a compacted and stabilized output. Detailsof the compaction are discussed in more detail below with respect toFIGS. 3-5.

FIG. 2 shows a processed image and corresponding histogram, inaccordance with an embodiment. Output image 200 is a processed imagecorresponding to raw image 100. Output image 200 has been compacted suchthat it has an output dynamic range (in this example, 8-bit) that islower than the dynamic range of the input signal. As shown by compacthistogram 202, the insignificant bins of histogram 102 have beenintelligently compacted. The original histogram, representing a 14 bitimage, has 16,384 bins each of which corresponds to a different tone.During compaction, these bins are mapped to the 256 bins shown in thecompacted histogram, representing the 8 bit compacted image, using thedata in the LUT. This compaction is performed intelligently, such thatmultiple insignificant 14 bit bins may be mapped to a single output 8bit bin. Thus, after compaction, the significant portion of the signaloccupies a larger proportion of the 8 bit dynamic range, as compared tothe 14 bit dynamic range as shown in FIG. 2.

FIG. 3 shows an overview of a method of digital signal dynamic rangereduction, in accordance with an embodiment of the invention. At block300, an input digital signal having an input dynamic range is received.This input dynamic range may be higher than is desired for a givenapplication (e.g., the input dynamic range may be too high for connecteddevices to support). Embodiments of the present invention are describedin which the digital signal is a digital video signal including asequence of frames, however the methods described can be equally appliedto reduce the dynamic range of other signals. At block 302, the digitalsignal is analyzed to determine a frame of the digital signal toprocess. For example, when the digital signal is initially received, aninitial frame of the signal is identified for processing and anyremaining frames of the signal can be processed sequentially thereafter.At block 304, the identified frame is processed on parallel processingpaths: an output processing path and an analysis processing path. Byprocessing each frame in parallel, the processed signal can be viewedwith latency substantially reduced or eliminated. For applications inwhich latency is not a concern, all statistics and the compactingfunction can be computed from a frame and then applied to the sameframe. When this is the case, the “previous” frame in the descriptioncan be replaced by the “current” frame.

At block 306 the dynamic range of the identified frame (e.g., frame(i),where ‘i’ is an integer) is compacted from the input (e.g., higher)dynamic range (e.g., 16-bits) to an output (e.g., lower) dynamic range(e.g., 8-bits) using a compacting function calculated based on aprevious frame (e.g., frame(i−1)). At block 308, the compacted frame isthen output to be displayed on a monitor or other system module(s). Thisreduces delay in real-time applications, enabling processed frames to beprocessed and viewed as they are received without first analyzing eachframe. In some embodiments, the compaction can be customized based onthe capabilities of the display. For example, in applications using an8-bit display, each frame can be compacted from its initial dynamicrange to an 8-bit output dynamic range; whereas in applications using a10-bit display, each frame can be compacted from its initial dynamicrange to a 10-bit dynamic range.

In some embodiments, while frame(i) is being compacted using datadetermined for frame(i−1), frame(i) can also be analyzed in parallel todetermine a new compacting function (such as a look-up table) to be usedto compact a subsequent frame, e.g., frame(i+1). At block 310, ahistogram for frame(i) is created and signal statistics (such as acentroid of the histogram, threshold values, and number of countsrelative to the centroid) are determined. Using the histogram, one ormore threshold values can be determined. The threshold value(s) can beused to define significant and insignificant bins of the histogram. Insome embodiments, the threshold value can be predefined. At block 312,the threshold value can be applied to the histogram to identifyinsignificant bins that include fewer counts than the threshold value.At block 314, based on the centroid, threshold values, and other signalstatistics determined previously, compacting statistics for thehistogram can be determined, such as a shift factor, a scale factor, anda centroid ratio. At block 316, the new compacting function can becomputed using the compacting statistics and stored. Once processing ofthe frame has completed, the method can return to block 302. Each framecan be processed sequentially until the last frame in the sequence hasbeen processed, at which point the method ends. This method is describedfurther below, with respect to FIG. 4.

FIG. 4 shows a block diagram 400 of a method of digital signal dynamicrange reduction, in accordance with an embodiment. A digital signal 402having an input dynamic range is received. The input dynamic range maybe higher than desired for a given application and, as a result, thedigital signal 402 may be compacted to a lower, output dynamic range(also referred to as “target dynamic range”). For the purpose ofclarity, the digital signal in the example of FIG. 4 is atwo-dimensional video signal comprising a sequence of image frames;however the method is equally applicable to other digital signals, suchas audio signals, three-dimensional video signals, and other digitalsignals to which dynamic range reduction is desirable. In the methodshown in FIG. 4, there is a one-frame latency on signal statistics. Thatis, each frame is compacted using statistics and data determined basedon the previous frame. The output signal, however, has no latency. Assuch, the block diagram includes two paths along which each frame isprocessed: an analysis path 400 a and an output path 400 b. Each framecan be processed along both paths in parallel.

On the analysis path 400 a, a histogram is generated that represents aframe of the digital signal. This histogram can be analyzed to determinesignal statistics that describe the distribution of the histogram (suchas the centroid of the histogram, one or more threshold values, etc.).Based on the signal statistics, compacting statistics for the histogramcan be determined, such as a shift factor, a scale factor, and acentroid ratio. The signal statistics and compacting statistics can bestored and used to create a compacting function (in this example, alookup table (LUT)) which is indexed by frame, to be used to compact asubsequent frame. In some embodiments, the compacting function can be aone dimensional table that maps bins in an input dynamic range to binsin an output dynamic range. For example, a LUT that maps from a 14-bitdynamic range to an 8-bit dynamic range can have 16,384 entries(corresponding to each possible tone in a 14-bit image) that each map toa value between 0-255 (corresponding to each possible tone in an 8-bitimage). The statistics can be used to determine the mapping embodied inthe LUT. Along the output path 400 b, the LUT for the previous frame,LUT(i−1), is retrieved and applied to the current frame(i). The signalelements (e.g., pixels) of frame(i) are mapped from the input, higherdynamic range to the output, lower dynamic range using the LUT for theprevious frame before being output to a display. The mapping reduces thedynamic range of the frame, by compacting insignificant portions of theframe while maintaining the distribution of the histogram and withoutintroducing artifacts and/or instability to the output signal. Theprocessing details of each path are described below in turn.

In some embodiments, at block 408 a representative histogram isgenerated as the frame data streams in rather than waiting for all ofthe frame data to be received. The representative histogram can includea predefined maximum bin cap. The bin cap can be used to quicklyidentify important bins without having to sort and count every signalcount (e.g., corresponding to each pixel, where the signal is an imageor video) that belongs to the bin. Additionally, while the frame datastreams in and the signal counts are sorted and put in sequence, asequential centroid of the data is calculated. This sequential centroidis updated as each unit of signal element (e.g., in the case of an imageor video, each pixel) is received and processed. Based on the sequentialcentroid, the number of counts on the left and right side of thehistogram, relative to the sequential centroid, is determined. Therepresentative histogram, sequential centroid, and number of signalcounts on the left/right sides of the histogram (relative to thecentroid) are updated as each unit of signal element is processed.

At block 410, statistics models of the histogram statistics, includingthe representative histogram, sequential centroid, and number of signalcounts on the left/right sides of the histogram, can be created and/orupdated. The statistics models can track the changes in the histogramstatistics over time (e.g., over a sequence of time indexed frames). Insome embodiments, the statistics models can include a threshold modelcalculated based on the histogram statistics. The threshold model caninclude one or more histogram thresholds determined for each frame ofthe digital signal. For example, a histogram threshold for a given framecan be calculated using a mean number of counts per bin on the left sideof the histogram, and a mean number of counts per bin on the right sideof the histogram. The threshold(s) can be applied to the bins of thehistogram to identify those bins which do not contribute significantlyto the signal because they are associated with a relatively small numberof counts.

In accordance with an embodiment, to address stability of the signal, atblock 410 a stabilization model (e.g. an exponential model) can becreated and/or updated based on the histogram statistics determined atblock 408. The stabilization model can be applied to the representativehistogram, sequential centroid, and/or threshold and can control howquickly or slowly the statistics models are updated. This improves theframe by frame stability of the data by limiting changes in intensityfrom frame to frame which can lead to rapid global or local signalfluctuation. An adaptive calculation process can be used to determinethe rate of change of the statistics models, and determine an updaterate based on the rate of change. The update rate can, for example, beused to scale the changes to the statistics calculated between frames.Changes to the statistics can be multiplied by the update rate. Thus,small changes multiplied by a small update rate are minimized; whereaslarge changes multiplied by a large update rate are quickly incorporatedinto the models. For example, the adaptive calculation process cancompare the left and right sides of the histogram from one frame withthe left and right sides of the histogram from the previous frame todetermine how similar the two frames are. As the differences betweenframes increase, the update rate is increased according to thestabilization model. This makes the stabilization model respond morequickly as new frame data is received. Similarly, as the differencesbetween frames decrease, the rate of change of the update rate candecrease. This way, the system can adapt to the changing data in acontrolled fashion, providing stability.

At block 414, a histogram centroid model is updated based on the newupdate rate. This allows the centroid to be updated in a controlledmanner. The histogram centroid model can track changes to the histogramcentroid over time (e.g., across a sequence of time indexed frames). Asdescribed above, the histogram centroid can correspond to a weightedaverage of the intensity of signal elements (e.g., pixels) in the frame.As such, small differences in intensity between frames, which result insmall differences between the frames' centroids, can result in anunstable output signal, which may be manifest as an observed local orglobal fluctuation in the signal. Lowering the update rate in responseto small changes between frames can reduce or eliminate any observedfluctuations. Similarly, large dynamic changes in intensity betweenframes (e.g., if a bright or dark image enters the frame), can result inlarge differences between the frames' centroids. If the centroid is notupdated quickly enough, a loss of clarity (such as ghosting) in theoutput signal may be observed. By increasing the update rate when largechanges are present, the clarity of the output signal is preserved.

At block 416, histogram thresholds for the frame can be determined. Asdescribed with respect to block 408, a histogram threshold for a givenframe can be calculated based on an average number of counts (e.g.,pixels) per bin. In some embodiments, a mean bin value (counts/bin) ofbins to the left of the histogram, and a mean bin value for bins to theright of the histogram, can be determined using a multiple pass (e.g.,four pass) iterative mean. The left- and right-side mean values can beused to determine a histogram threshold. In some embodiments, the leftand right sides of the histogram can each have a different threshold,based on the mean bin value on each side of the histogram. For example,the threshold value can be a percentage of the mean bin value. Thethreshold(s) can then be applied to the bins of the histogram toidentify those bins which do not contribute significantly to the signalbecause they are associated with a relatively small number of counts.Once the histogram threshold values have been determined for the currentframe, the thresholds can be compared with the threshold modelspreviously calculated, and the update rate determined at block 412 canbe applied to the difference between the current thresholds and thethreshold models to produce stabilized thresholds. This allows for acontrolled change to the threshold values from one frame to the next.The threshold models can then be updated with the stabilized thresholdsfor the current frame.

At block 418, the stabilized thresholds can be applied to the histogramby determining a dynamic range compaction ratio for each bin in thehistogram. This compaction ratio is determined by dividing the value ofthe bin by the stabilized threshold value. The compaction ratioindicates whether a bin is significant or insignificant. Significantbins can have a compaction ratio greater than one, and insignificantbins can have a compaction ratio less than one. Once the significant andinsignificant bins have been identified using the compaction ratio, thenumber of significant bins and compacted insignificant bins on the leftand right sides of the centroid and the width of the data can bedetermined. The width of the data can correspond to the number ofsignificant bins. This can be used to define a shift for mapping thesignal data from the raw data dynamic range, to the compacted dynamicrange. Thereafter, a centroid ratio can be defined based on the numberof compacted bins in the shift, and based on the raw dynamic range andthe target dynamic range. The centroid ratio can be used to map the bincontaining the centroid in the raw, higher dynamic range, to theappropriate bin in the target, lower dynamic range. Additionally, ascale factor can be calculated which is used to shape the data so thatit fits in the smaller dynamic range. The centroid ratio, shift, andscale factor are then stored for use on the subsequent frame.

At block 420, a compacting function (such as a LUT) can be built for thecurrent frame using the data (e.g., the scale factor, centroid ratio,and shift) calculated at block 418. As described above, the compactingfunction can be a data structure, such as a one dimensional table,array, or other suitable data structure, that maps bins in the rawdynamic range to bins in the target dynamic range. The centroid ratio isused to map the bin of the centroid to a corresponding bin of the targetdynamic range in the compacting function. The shift and scale factorscan be used to map and scale the rest of the data to fit in a targetrange in the target dynamic range. The less significant andinsignificant bins are compacted into fewer output bins in the targetdynamic range, resulting in the significant bins occupying a largerproportion of the target dynamic range. In some embodiments, counts fromconsecutive insignificant bins in the input dynamic range can becompacted until a total counts in the compacted bin in the targetdynamic range reaches the threshold value. The compacting function thenmaps the bins from the input dynamic range to the compacted bin in thetarget dynamic range (e.g., bins 16,320-16,329 in a 14-bit image can bemapped to bin 252 in an 8-bit image). This intelligent dynamic rangecompaction also results in a global automatic gain and level effect onthe compacted data. The compacted frame can then be output to a displayor other system module(s) to be viewed or further processed.

FIG. 5 shows a method of signal optimization, in accordance with anembodiment of the invention. As described above, at block 406 one ormore optimizations can be applied to the compacted frame. FIG. 5 showsone optimization that can be applied to each compacted frame. As shownin FIG. 5, at block 502 a histogram of the compacted frame is generated.At block 504 an optimization function (such as log 2) is applied to thehistogram of the compacted frame, resulting in an optimized histogram(Histogram_(opt)). To prevent over-optimization, at block 506, a lookuptable (LUT_(opt)) is generated based on a combination of the optimizedhistogram and the histogram of the compacted frame. This combinationcontrols the optimization of the compacted signal without producingundesirable results. The optimized lookup table (LUT_(opt)) maps binsfrom the histogram of the compacted frame (in the target dynamic range)to bins of an optimized histogram (also in the target dynamic range). Insome embodiments, the user can dynamically adjust the strength of theoptimized image by changing the combination of the optimized histogramand the histogram of the compacted frame.

FIG. 6 shows an imaging system 600 for digital signal dynamic rangereduction, in accordance with an embodiment of the invention. Imagingsystem 600 can represent a portable device which can be carried by auser, such as a digital camera device, or a device that can beincorporated into a larger system such as an unmanned aerial vehicle(UAV) or other system. Imaging system 600 can include an image capturedevice 602 which can include a lens 604 and a sensor 606, such as acharge coupled device (CCD) or complementary metal-oxide-semiconductor(CMOS) sensor, configured to capture video. The image capture device 602can be integrated with, or otherwise connected to, an image processingdevice 608. Image processing device 608 can include a memory 610 thatincludes instructions for performing a method of dynamic rangereduction, such as that described with respect to FIGS. 3-5, on imagesprovided by image capture device 602. Memory 610 can further storehistogram statistics calculated for each frame of video during dynamicrange reduction, and can store one or more lookup tables 616 used duringdynamic range reduction. In some embodiments, memory 610 can beintegrated into image processing device 608, in other embodiments all ora portion of the contents of memory 610 can be stored in a remote datastore accessible to the image processing device 608. Image processing608 can include a processor 618, such as a general purposemicroprocessor, a special purpose microprocessor, an image processor, adigital signal processor (DSP), and/or field programmable gate arrays(FPGAs), or other suitable components. Processed images can be passedfrom image processing device 608 to display 620 to be displayed to auser. In some embodiments, the display 620 can be integrated with theimage processing device 608, and/or the image capture device 602. Insome embodiments, display 620 can be connected to image processingdevice 608 remotely, such as through a network connection or a wirelessconnection.

FIG. 7 shows a system for digital signal dynamic range reduction, inaccordance with an embodiment of the invention. FIG. 7 is a simplifiedblock diagram of a computing system 700 that may be used in accordancewith embodiments of the present invention. In accordance with anembodiment, computing system 700 can be incorporated into a device, suchas imaging system 600, described above.

Computer system 700 is shown comprising hardware elements that may beelectrically coupled via a bus 724. The hardware elements may includeone or more central processing units (CPUs) 702, one or more inputdevices 704 (e.g., a mouse, a keyboard, touchscreen, etc.), and one ormore output devices 706 (e.g., a display device, a printer, etc.). Thecomputer system may also include a digital imaging system 726, such asthermal imager. The digital imaging system can include a lens 728, animage processor 730 and a digital image sensor such as a charge coupleddevice (CCD) or a complementary metal-oxide-semiconductor (CMOS) sensor.The CPUs may include single or multicore CPUs. Additionally, oralternatively, the computer system 700 may be implemented using one ormore digital signal processors (DSPs) 734 and/or field programmable gatearrays (FPGAs) 736. Computer system 700 may also include one or morestorage devices 708. By way of example, the storage device(s) 708 mayinclude devices such as disk drives, optical storage devices, andsolid-state storage devices such as a random access memory (RAM), aread-only memory (ROM), and/or Electrically Erasable ProgrammableRead-Only Memory (EEPROM), which can be programmable, flash-updateableand/or the like.

Computer system 700 may additionally include a computer-readable storagemedia reader 712, a communications subsystem 714 (e.g., a modem, anetwork card (wireless or wired), an infra-red communication device,etc.), and working memory 718, which may include RAM and ROM devices asdescribed above. In some embodiments, computer system 700 may alsoinclude a processing acceleration unit 716, which can include a digitalsignal processor (DSP), a special-purpose processor, Field ProgrammableGate Array (FPGA) and/or the like.

Computer-readable storage media reader 712 can further be connected to acomputer-readable storage medium 710, together (and, optionally, incombination with storage device(s) 708) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containingcomputer-readable information. Communications system 714 may permit datato be exchanged with a wireless or wired network and/or any othercomputer connected to that network. In particular, the communicationssystem 714 is operable to, e.g., receive instructions and transmit imageand/or video data captured using camera system 726.

Computer system 700 may also comprise software elements, shown as beingcurrently located within working memory 718, including an operatingsystem 720 and/or other code 722, such as an application program (whichmay be a client application, Web browser, etc.). In an exemplaryembodiment, working memory 718 can include the lookup table and theimage processing methods described above with respect to FIGS. 1-5. Itshould be appreciated that alternative embodiments of computer system700 may have numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets), or both. Further, connection to other computing devicessuch as network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules, orother data. Examples of storage and computer-readable media include RAM,ROM, EEPROM, flash memory or other memory technology, Compact DiscRead-only memory (CD-ROM), digital versatile disk (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or any other memory medium which can beused to store the desired information and which can be read by acomputer. Storage media and computer readable media may includenon-transitory memory devices.

Having disclosed several embodiments, it will be recognized by those ofskill in the art that various modifications, alternative constructions,and equivalents may be used without departing from the spirit of thedisclosed embodiments. Additionally, a number of well-known processesand elements have not been described in order to avoid unnecessarilyobscuring the present technology. Accordingly, the above descriptionshould not be taken as limiting the scope of the technology.

As used herein and in the appended claims, the singular forms “a”, “an”,and “the” include plural references unless the context clearly dictatesotherwise. Thus, for example, reference to “a processor” includes aplurality of such devices, and reference to “the subsystem” includesreferences to one or more subsystems and equivalents thereof known tothose skilled in the art, and so forth.

What is claimed is:
 1. A method of a dynamic range compaction,comprising: receiving a digital signal having a first dynamic range;compacting a frame of the digital signal from the first dynamic range toa second dynamic range using a compacting function; creating a histogramfor the frame and based on the histogram identifying a centroid and oneor more thresholds; applying the one or more thresholds to each bin inthe histogram to determine a compaction ratio for each bin by dividing avalue of each bin by one of the thresholds; identifying insignificantbins using a compaction ratio for each bin; and compacting theinsignificant bins by mapping a plurality of insignificant bins from ahistogram for the frame to an output bin in a histogram representing thesecond dynamic range using the compacting function.
 2. The method ofclaim 1, wherein compacting a frame of the digital signal from the firstdynamic range to a second dynamic range using a compacting functioncomprises: retrieving a lookup table; and mapping counts from each binin the histogram to a plurality of bins in a histogram representing thesecond dynamic range.
 3. The method of claim 1, further comprising:building a new compacting function for compacting a subsequent frameusing compacting statistics.
 4. The method of claim 3, wherein thecompacting statistics include a shift factor, scale factor, and centroidratio for the histogram.
 5. The method of claim 3, wherein build a newcompacting function comprises: determining a bin in the second dynamicrange corresponding to the centroid; and mapping significant bins fromthe histogram to the second dynamic range using a shift factor.
 6. Themethod of claim 3, further comprising: compacting the subsequent frameusing the new compacting function.
 7. The method of claim 1, furthercomprising: outputting the compacted frame to a display, wherein thedisplay is configured to display signals having the second dynamicrange.
 8. A system for dynamic range compaction, comprising: a signalcapture device configured to output a digital signal having a firstdynamic range; a signal processing device configured to receive thedigital signal from the signal capture device, and wherein the signalprocessing device is further configured to: compact a frame of thedigital signal from the first dynamic range to a second dynamic rangeusing a compacting function; create a histogram for the frame and basedon the histogram identifying a centroid and one or more thresholds bydividing a value of each bin by one of the first thresholds; apply theone or more thresholds to each bin in the histogram to determine acompaction ratio for each bin; identify insignificant bins using acompaction ratio for each bin; and compact the insignificant bins bymapping a plurality of insignificant bins from a histogram for the frameto an output bin in a histogram representing the second dynamic rangeusing the compacting function.
 9. The system of claim 8, wherein tocompact a frame of the digital signal from the first dynamic range to asecond dynamic range using a compacting function, the signal processingdevice is further configured to: retrieve a lookup table; and map countsfrom each bin in the histogram to a plurality of bins in a histogramrepresenting the second dynamic range.
 10. The system of claim 8,wherein the signal processing device is further configured to: build anew compacting function for compacting a subsequent frame usingcompacting statistics.
 11. The system of claim 10, wherein thecompacting statistics include a shift factor, scale factor, and centroidratio for the histogram.
 12. The system of claim 10, wherein to build anew compacting function, the signal processing device is furtherconfigured to: determine a bin in the second dynamic range correspondingto the centroid; and map significant bins from the histogram to thesecond dynamic range using a shift factor.
 13. The system of claim 10,wherein the signal processing device is further configured to: compactthe subsequent frame using the new compacting function.
 14. The systemof claim 8, further comprising: a display configured to receive thecompacted frame from the signal processing device, wherein the displayis configured to display signals having the second dynamic range.
 15. Anon-transitory computer readable storage medium including instructionsstored thereon which, when executed by a processor, cause the processorto perform operations including: receiving a digital signal having afirst dynamic range; compacting a frame of the digital signal from thefirst dynamic range to a second dynamic range using a compactingfunction; creating a histogram for the frame and based on the histogramidentifying a centroid and one or more thresholds; applying the one ormore thresholds to each bin in the histogram to determine a compactionratio for each bin by dividing a value of each bin by one of thethresholds; identifying insignificant bins using a compaction ratio foreach bin; and compacting the insignificant bins by mapping a pluralityof insignificant bins from a histogram for the frame to an output bin ina histogram representing the second dynamic range using the compactingfunction.
 16. The non-transitory computer readable storage medium ofclaim 15, wherein compacting a frame of the digital signal from thefirst dynamic range to a second dynamic range using a compactingfunction comprises: retrieving a lookup table; and mapping counts fromeach bin in the histogram to a plurality of bins in a histogramrepresenting the second dynamic range.
 17. The non-transitory computerreadable storage medium of claim 15, wherein the operations furthercomprises: building a new compacting function for compacting asubsequent frame using compacting statistics.
 18. The non-transitorycomputer readable storage medium of claim 15, wherein the compactingstatistics include a shift factor, scale factor, and centroid ratio forthe histogram.
 19. The non-transitory computer readable storage mediumof claim 15, wherein building a new compacting function comprises:determining a bin in the second dynamic range corresponding to thecentroid; and mapping significant bins from the histogram to the seconddynamic range using a shift factor.
 20. The non-transitory computerreadable storage medium of claim 15, wherein the operations furthercomprises: compacting the subsequent frame of the digital signal usingthe new compacting function.